Detecting outliers in DEA after correcting biases in efficiency scores using simulated beta samples (Q2073478)

Summary: We address the issue that data used in DEA are possibly contaminated with statistical noise, and as a result, efficiency scores deviate from actual values due to statistical bias. In such situation, detecting outliers could be misleading. Therefore, we propose a method to correct the biases and compute DEA efficiency scores that follow underlying beta distributions, and we use \textit{R. G. Thompson} et al.'s [Ann. Oper. Res. 68, 303--327 (1996; Zbl 0867.90004)] DEA model with assurance regions. Then, we demonstrate the bias-correction process of the DEA frontier estimates by constructing confidence intervals for the mean scores and use them to detect outliers.